Field of the Invention
The present invention concerns methods and systems for magnetic resonance imaging, and in particular concerns methods and systems for implementing motion correction in magnetic resonance imaging of large joints, such as the knee.
Description of the Prior Art
Magnetic resonance (MR) imaging is a known technology with which images from the interior of an examination subject, such as a patient, can be generated. Simply described, the examination subject is placed in a magnetic resonance imaging scanner in which a strong, static, homogenous basic magnetic field is generated, typically having a field strength of 0.2 through 7 or more Tesla, causes nuclear spins in the subject to be oriented along the field lines of the basic magnetic field. In order to trigger magnetic resonance signals, the examination subject is irradiated with radio-frequency excitation pulses (RF pulses), that cause the nuclear spins to deviate from the aligned orientation produced by the basic magnetic field. As the excited nuclear spins relax (i.e., return to the original orientation), they emit magnetic resonance signals. These magnetic resonance signals are also RF signals, and are detected by one or more suitable antennas, as raw data. The detected raw data are entered into a memory that represents a mathematical domain known as k-space and MR images are reconstructed on the basis of the k-space data, typically by a Fourier transformation of the raw data in k-space into image data. The image data are then used to display an image of the anatomy within the field of view from which the MR signals were acquired.
Motion of the patient that occurs within the field of view while the MR raw data are being acquired degrades the quality of the resulting image. Such patient motion typically leads to artifacts in the reconstructed image, such as blurring and ghosting.
Many techniques are known in the field of magnetic resonance imaging for correcting or compensating for motion that occurs within the field of view from which MR data have been acquired. Known motion compensation techniques can be generally divided into two categories: (i) prospective motion correction, wherein the imaging field of view (FOV) is updated “on the fly” during acquisition of the MR raw data, and (ii) retrospective motion correction, wherein the motion correction is performed after the acquisition of the MR raw data, i.e., during image reconstruction.
Common to both prospective and retrospective techniques is that they both require a measurement of the motion that occurs during the scan, i.e., during the acquisition of the MR raw data. Such motion detection can be performed either with MR-based techniques, (called MR navigators), or with external motion tracking systems, such as optical tracking.
MR navigator signals are resonant signals that are acquired from the subject in the MR scanner from a suitably-sized volume of the subject, which is susceptible to the motion that is being tracked. The MR navigator signals can be acquired and processed very quickly and thus are available for use during the actual acquisition of the MR data, such as for adjusting the field of view.
The use of MR-based navigators has been extensively developed for neuroimaging, and can range from acquiring one-dimensional projections in k-space, as described in the article by Kober et al. “Head Motion Detection Using FID Navigators,” Magnetic Resonance in Medicine, Vol. 66, pp. 135-143 (2011) to full 3D low special resolution images acquired with techniques such as 3D EPI, as described in the article by Tisdall et al. “Volumetric Navigators for Prospective Motion Correction and Selective Reacquisition in Neuro Anatomical MRI,” Magnetic Resonance in Medicine, Vol. 68, pp. 389-399 (2012). A further technique is described in Bhat et al. “Simultaneous Multi-Slice (SMS) Accelerated EPI Navigators for Prospective Motion Correction in the Brain,” Proceedings of the ISMRM 23rd Annual Meeting and Exhibition (2015) p. 5020 all of these MR navigator methods rely on the assumption that the motion of the object being tracked is rigid in nature, and thus can be modeled with six degrees of freedom (three rotations and three translations). This rigid body assumption is valid in neuroimaging, wherein the brain can be assumed to be a rigidly moving object (i.e., the entire brain moves in whatever direction is detected). For MR imaging in orthopedic applications, however, such as for obtaining MR images of a knee or an elbow, this rigid body assumption is not valid.